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Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

ABSTRACT: CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype. CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

SUBMITTER: Lenoir W

PROVIDER: S-EPMC8586352 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAcute myeloid leukemia (AML) is a highly aggressive hematological malignancy characterized by extensive genetic abnormalities that might affect the prognosis and provide potential drug targets for treatment. Reprogramming of lipid metabolism plays important roles in tumorigenesis and progression and has been newly recognized a new hallmark of malignancy, and some related molecules in the signal pathways could be prognostic biomarkers and potential therapeutic targets for cancer treatment. However, the clinical value of lipid metabolism reprogramming in AML has not been systematically explored. In this study, we aim to explore the clinical value of lipid metabolism reprogramming and develop a prognostic risk signature for AML.MethodsWe implemented univariate Cox regression analysis to identify the prognosis-related lipid metabolism genes, and then performed LASSO analysis to develop the risk signature with six lipid metabolism-related genes (LDLRAP1, PNPLA6, DGKA, PLA2G4A, CBR1, and EBP). The risk scores of samples were calculated and divided into low- and high-risk groups by the median risk score.ResultsSurvival analysis showed the high-risk group hold the significantly poorer outcomes than the low-risk group. The signature was validated in the GEO datasets and displayed a robust prognostic value in the stratification analysis. Multivariate analysis revealed the signature was an independent prognostic factor for AML patients and could serve as a potential prognostic biomarker in clinical evaluation. Furthermore, the risk signature was also found to be closely related to immune landscape and immunotherapy response in AML.ConclusionsOverall, we conducted a comprehensive analysis of lipid metabolism in AML and constructed a risk signature with six genes related to lipid metabolism for the malignancy, prognosis, and immune landscape of AML, and our study might contribute to better understanding in the use of metabolites and metabolic pathways as the potential prognostic biomarkers and therapeutic targets for AML.

Project description:Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method's performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets.

Project description:BackgroundAs a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.MethodsFirst, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.ResultsEight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.ConclusionsThis study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

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Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

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